Google Ads

Make sure your file size is no larger than 150K.

Responsive display ad (image)

• 1.91:1, minimum 600 x 314
• 1:1, minimum 300 x 300

Responsive display ad (logo)

• 1:1, minimum 128 x 128
• 4:1, minimum 512 x 128

Common Image Ad Sizes:

Make sure your file size is no larger than 150K

Image ads

• 300 x 100
• 750 x 300
• 750 x 200
• 750 x 100
• 950 x 90
• 88 x 31
• 220 x 90
• 300 x 31
• 980 x 90
• 240 x 133
• 200 x 446
• 292 x 30
• 960 x 90
• 970 x 66
• 300 x 57
• 120 x 60
• 320 x 400
• 600 x 314
• 468 x 60
• 728 x 90
• 250 x 250
• 200 x 200
• 336 x 280
• 300 x 250
• 120 x 600
• 160 x 600
• 320 x 50
• 320 x 100
• 300 x 50
• 425 x 600
• 300 x 600
• 970 x 90
• 240 x 400
• 980 x 120
• 930 x 180
• 250 x 360
• 580 x 400
• 300 x 1050
• 480 x 320
• 320 x 480
• 768 x 1024
• 1024 x 768
• 480 x 32
• 1024 x 90
• 970 x 250
• 375 x 50
• 414 x 736
• 736 x 414

Google Ads attribution models show fractional conversions because they distribute credit for conversions across multiple touchpoints in the customer’s journey. In online advertising, the customer journey is typically a series of interactions with your ads and website before a conversion occurs. Attribution models are used to determine how much credit each interaction should receive in contributing to that conversion.

Here’s why some Google Ads attribution models may show fractional conversions:

1. Multiple Touchpoints: In today’s digital landscape, customers often interact with various ads and touchpoints before making a purchase or completing a conversion. For example, a user might click on an ad, visit the website, leave, and return later through another ad or organic search before purchasing. Attribution models account for these multiple touchpoints and distribute credit accordingly.

2. Credit Sharing: Attribution models aim to attribute conversions reasonably to all the touchpoints that contributed to the conversion. Instead of assigning 100% of the credit to the last click (as in the last-click attribution model), fractional attribution models distribute credit across all interactions that play a role in the customer’s decision-making process.

3. Multi-Channel Campaigns: With the rise of multi-channel marketing, users may interact with ads on different platforms and devices. Fractional attribution models allow advertisers to evaluate the combined effect of their marketing efforts across various channels and devices.

4. Tailored Attribution: Different businesses have unique customer journeys and conversion paths. Fractional attribution models, like data-driven attribution, consider your audience’s specific behavior patterns and allocate credit accordingly.

In summary, fractional conversions in Google Ads attribution models provide a more comprehensive view of the customer journey and the interactions that lead to conversions. By fairly distributing credit across touchpoints, marketers gain valuable insights into the effectiveness of their ad campaigns and can make data-driven decisions to optimize their strategies for better results.

When optimizing a Google Ads account, the Cost Per Click (CPC) can increase due to various factors. Here are some reasons why CPC can go up during the optimization process:

1. Improved Ad Position: Your ads may need higher positions in the search results, often leading to higher CPCs. Higher ad positions generally yield better visibility and click-through rates but may come at a higher cost.
2. Increased Competition: As you optimize your account and refine your targeting, you might enter more competitive auctions. Higher competition can drive up the bidding competition, leading to increased CPCs. This often occurs in highly saturated markets or during peak times when multiple advertisers are vying for the same audience.
3. Expanded Targeting: When optimizing an account, you may expand your targeting options, such as targeting additional keywords, geographic locations, or increasing budgets. While this broader targeting can increase your reach, it can also increase competition and temporarily drive up CPCs as more advertisers target the exact keywords or audience segments.
4. Seasonal Demand: Depending on your industry or business, certain times of the year may experience increased demand and competition. Advertisers may increase bids during peak seasons or events to capture potential customers’ attention, resulting in higher CPCs.

Learning Mode is Yuck

It’s important to understand that when you create a new Google Ads campaign or make significant changes to an existing campaign, it may go into a “learning mode.” Learning mode occurs when Google’s machine learning algorithms need to gather data and understand how your campaign performs to optimize its delivery and show your ads to the right audience. Here’s why triggering learning mode can have potential drawbacks:

1. Limited performance: During learning mode, your campaign’s performance may be suboptimal compared to fully optimized. This is because Google is experimenting and testing different strategies to learn what works best for your campaign. The algorithm might take time to gather sufficient data and make informed decisions.
2. Impacted ad delivery: When a campaign is in learning mode, Google may restrict the frequency or volume of your ad delivery. Your ads may be shown less frequently or to fewer users than during regular operation. This can limit your campaign’s reach and potential for generating conversions.
3. More extended optimization period: Learning mode requires patience, as it can take time for the algorithm to gather enough data and optimize your campaign’s performance. The duration of the learning mode varies depending on factors such as your campaign settings, budget, and conversion volume. Giving the algorithm enough time to learn and optimize your campaign effectively is essential.

Once the learning period is complete and the algorithm has gathered sufficient data, it can make more informed decisions and deliver your ads more effectively.

To navigate the challenges of learning mode, we closely monitor your campaign’s performance, make incremental changes daily rather than frequent significant adjustments, and ensure that your campaign settings align with your advertising goals. Additionally, we focus on creating relevant ad copy, targeting the right audience, and optimizing your landing pages to maximize the effectiveness of your campaign.

As we gain more data, we can make data-driven decisions, and your campaigns will become more refined and deliver better results.